An appendix is included of a computer program listing. The total number of pages is four.
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The present invention generally concerns techniques which provide an objective assessment of a predefined subject. More specifically, the present invention concerns the assessment of the predefined subject utilizing predefined core characteristics whose analysis depends on information inquiries.
Information analysis is an important tool that is used by various entities such as governments, businesses, universities and individuals, to aid in their choice of a future course of action. In this regard, entities often collect and manage huge amounts of information. Entities utilize this information in multiple endeavors such as predicting trends in the stock market, forecasting the weather, and determining whether or not a business is likely to succeed in a certain community. The value of the information utilized by the entities, however, relates directly to how well the information is analyzed. In other words, the better the analysis, the more accurately entities can predict the future event.
To acquire information for analysis, surveys are often used. Thereafter, known techniques to analyze survey information vary. For example, a traditional linear analysis tabulates and analyzes answers to a question independently of other questions. Therefore, linear analysis typically ends with a pie chart or a bar graph representing data for a single question, as shown in FIG. 1. A problem exists in that it is often difficult to determine what questions to ask. In addition, the findings often produce a lack of insight and depend on an interviewer""s skill. Moreover, the questions asked do not relate to one another in a meaningful way.
Another known analyzing technique is cross-tab analysis. Cross-tab analysis attempts to determine whether two or more variables are independent of each other or associated. During cross-tab analysis, a question is subdivided into categories such as age ranges, professional affiliation and industry type. Cross-tab analysis attempts to determine whether a response changes as the respondent""s group changes. Thereafter, given a predetermined expectation for each variable, a researcher compares each outcome of the cross-tab analysis to an expected outcome.
A problem exists with known analysis techniques when an answer to a core question, or a conclusion to a core characteristic, depends on the answers of more than one inquiry. For example, several questions are necessary to adequately define a core characteristic when there is a need to better understand a company and the company""s industry within a community. This problem is further complicated when the information inquiries provide subjective information that is not equally important to the conclusion of the core characteristics. Neither linear analysis nor cross-tab analysis accommodates such situations.
Accordingly, there is a need for a method of analysis which provides for an evaluation of a predefined subject which is described in terms of predetermined core characteristics. In addition, there is a need for a method of analysis that correlates multiple information inquiries to one or more core characteristic. Moreover, there is a need for a method of analysis that accommodates weighting of the information inquiries and the outcome of the analysis does not depend on the skill of the interviewer.
Such needs are met or exceeded by the present method of matrix analysis. A method for information analysis is shown and described which accommodates answers to core questions, even when the answers depend on information from multiple inquiries, and the importance of the different inquiries varies.
More specifically, the present invention evaluates the importance of the predefined subject utilizing a first data set that contains general characteristics describing the subject. Moreover, the present matrix analysis utilizes a second data set that contains information inquiries. Each information inquiry is related to one or more of the predefined core characteristics of the subject. Thereafter, matrix analysis is performed using the first and second data sets to generate an importance value for each predefined core characteristic.